This study addresses the challenges associated with single-system long-baseline real-time kinematic (RTK) navigation, including limited positioning accuracy, inconsistent signal reception, and significant residual atmospheric errors following double-difference corrections. This study explores the effectiveness of long-baseline RTK navigation using an integrated system of the BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), and Galileo Satellite Navigation System (Galileo). A long-baseline RTK approach that incorporates Kalman filtering and partial ambiguity resolution is applied. Initially, error models are used to correct ionospheric and tropospheric delays. The zenith tropospheric and inclined ionospheric delays and additional atmospheric error components are then regarded as unknown parameters. These parameters are estimated together with the position and ambiguity parameters via Kalman filtering. A two-step method based on a success rate threshold is employed to resolve partial ambiguity. Data from five long-baseline IGS monitoring stations and real-time measurements from a ship were employed for the dual-frequency RTK positioning experiments. The findings indicate that integrating additional GNSSs beyond the BDS considerably enhances both the navigation precision and the rate of ambiguity resolution. At the IGS stations, the integration of the BDS, GPS, and Galileo achieved navigation precisions of 2.0 cm in the North, 5.1 cm in the East, and 5.3 cm in the Up direction while maintaining a fixed resolution exceeding 94.34%. With a fixed resolution of Up to 99.93%, the integration of BDS and GPS provides horizontal and vertical precision within centimeters in maritime contexts. Therefore, the proposed approach achieves precise positioning capabilities for the rover while significantly increasing the rate of successful ambiguity resolution in long-range scenarios, thereby enhancing its practical use and exhibiting substantial application potential.

To address the challenge of multimodal anomaly data governance in ship maintenance-cost prediction, this study proposes a three-stage hybrid data-cleansing framework integrating physical constraints and intelligent optimization. First, we construct a multi-dimensional engineering physical constraints rule base to identify contradiction-type anomalies through ship hydrodynamics validation and business logic verification. Second, we develop a Feature-Weighted Isolation Forest Algorithm (W-iForest) algorithm that dynamically optimizes feature selection strategies by incorporating rule triggering frequency and expert knowledge, thereby enhancing detection efficiency for discrete-type anomalies. Finally, we create a Genetic Algorithm-Ant Colony Optimization Collaborative Random Forest (GA-ACO-RF) to resolve local optima issues in high-dimensional missing data imputation. Experimental results demonstrate that the proposed method achieves a physical compliance rate of 88.2% on ship-maintenance datasets, with a 25% reduction in RMSE compared to conventional prediction methods, validating its superior data governance capability and prediction accuracy under complex operating conditions. This research establishes a reliable data preprocessing paradigm for maritime operational assurance, exhibiting substantial engineering applicability in real-world maintenance scenarios.

The development of equipment research projects is replete with uncertainties, complicating the provision of accurate and objective evaluations of their development costs. To enhance the precision of cost predictions for such projects, it is critical to assess and integrate the cost‐risk level. This study introduces an improved two‐dimensional cloud model (TDCM) that effectively integrates qualitative concepts and quantitative data for analyzing equipment projects. First, the principal factors influencing cost are thoroughly evaluated, and an assessment index system is structured with three primary and twelve secondary indicators. The probability of budget overrun and its impact are designated as the primary variables for assessing each indicator, aligning with the definition of cost risk. Second, this research merges the fuzzy analytic hierarchy process (FAHP) with the criteria importance though intercriteria correlation (CRITIC) method, employing coalitional game theory to ascertain the weights of each assessment indicator. Third, a TDCM is developed to derive cloud eigenvalues, and a two‐dimensional cloud diagram is constructed through MATLAB to preliminarily ascertain the risk level, with the degree of nearness subsequently calculated to refine these results. Fourth, each indicator in the assessment system is treated as a node to construct a Bayesian network (BN) based on logical relationships, and a sensitivity analysis is conducted to identify sensitive indicators. Fifth, the development of a specific mine countermeasure (MCM) weapon system is examined as a case study, incorporating relevant existing data into the improved model. The validity and feasibility of the model are corroborated by comparing it with traditional methods. The results affirm that the enhanced TDCM effectively navigates the ambiguity and randomness inherent in cost‐risk assessment data, providing a reference for similar scientific research projects.

The development process of equipment research projects is fraught with uncertainties, making it challenging to provide an accurate and objective evaluation of their development costs. To enhance the accuracy of cost prediction for equipment research projects, it is essential to consider the cost risk level and conduct relevant research on cost risk assessment. This study proposes an improved two-dimensional cloud model that integrates qualitative concepts and quantitative data for researching equipment projects. An assessment index system is constructed with three primary indicators and twelve secondary indicators. The main factors affecting the cost are comprehensively considered. The probability of overspending and the degree of impact of overspending are taken as the basic variables for assessing each indicator according to the definition of cost risk. The study combined the Fuzzy Analytic Hierarchy Process (FAHP) method with the Criteria Importance Though Intercrieria Correlation (CRITIC) method using the cooperative game method to determine the weights of each assessment indicator. Subsequently, a two-dimensional cloud model was established to obtain the cloud eigenvalues. A two-dimensional cloud diagram was drawn through MATLAB to initially determine the risk level, and the closeness was calculated to accurately determine the results. Finally, using a specific anti-mine system as an example, we verified the validity and feasibility of the model by comparing it with the traditional method. The results demonstrate that the improved two-dimensional cloud model can overcome the ambiguity and randomness of data in cost risk assessment and can provide a reference for other scientific research projects of the same type.

Software effort estimation (SEE) serves as a cornerstone of effective software project management, and case-based reasoning (CBR) stands out as one of the most extensively adopted approaches within this domain. Nevertheless, CBR-based SEE models are still plagued by two critical challenges: conventional case retrieval mechanisms lack the ability to differentiate the relative importance of various features, and data scarcity remains a persistent bottleneck. Both issues significantly compromise the estimation accuracy and interpretability of the models. To address these limitations, we propose a SHAP–Mixup synergistic framework that enhances both feature-aware similarity learning and data distribution modeling. Specifically, we introduce (1) a stability-aware SHAP-weighted similarity metric that integrates both the magnitude and variance of feature contributions to improve retrieval robustness, and (2) a density-aware Mixup augmentation strategy that generates synthetic samples guided by local data manifold structure rather than random interpolation. Experimental results on seven benchmark datasets demonstrate that the proposed method reduces MAE and MSE by up to 20.2% on average compared to baseline CBR models, while consistently improving Pred(0.25). Furthermore, by enhancing model interpretability, the proposed method equips project managers with actionable insights into the key drivers of software effort, thereby facilitating more informed and efficient resource allocation. Building on these findings, this study provides a novel and effective pathway for developing SEE models that are more accurate, robust, and transparent.

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis.